Adjustably Robust Optimal Power Flows with Demand Uncertainty via Path-based Flows

TL;DR

This paper develops a robust optimization framework for optimal power flow problems considering demand uncertainty, using path-based flows to improve decision-making under uncertain conditions.

Contribution

It introduces a novel two-stage adjustable robust formulation for power flows that incorporates line-use and flow variables with path-based flow modeling.

Findings

Enhanced robustness in power flow optimization under demand variability

Effective modeling of line expansion and switching decisions

Improved computational tractability for large-scale problems

Abstract

We study the optimal power flow problem with switching (or, equivalently, the line expansion problem) under demand uncertainty. Specifically, we consider the line-use variables at the first stage and the current- or power-flow at the second stage of two affinely adjustably robust formulations.